At CloudFactory, we are a mission-driven team passionate about unlocking the disruptive potential of AI for the world. By combining advanced technology with a global network of talented experts, we make unusable data usable and inference reliable and trustworthy, driving real-world business value at scale. 

More than just a workplace, we’re a global community founded on strong relationships and the belief that meaningful work transforms lives. Our commitment to earning, learning, and serving fuels everything we do, as we strive to connect one million people to meaningful work and build leaders worth following.

Our Culture

At CloudFactory, we believe in building a workplace where everyone feels empowered, valued, and inspired to bring their authentic selves to work. We are:

Mission-Driven: We focus on creating economic and social impact. People-Centric: We care deeply about our team’s growth, well-being, and sense of belonging. Innovative: We embrace change and find better ways to do things, together. Globally Connected: We foster collaboration between diverse cultures and perspectives.

If you’re ready to earn, learn, serve, and be part of a vibrant global community, CloudFactory is your place!

About the Role:

We are seeking a Junior ML Ops Engineer to join our team and help us provide a service designed to ensure ML models remain operational in production environments. The primary objective is to maintain the continuous performance of these models with minimal disruption to the client’s business goals, specifically focused on hiring and retaining transportation drivers. As customers expand their ML models, this service will scale to meet their needs.

Our approach combines proprietary technology with expert talent to deliver a best-in-class platform for machine learning, paired with scalable processes, configuration, and systems. 

A Complete Machine Learning Platform

Submit and deploy models - data scientists can easily upload their trained models and leverage the platform’s automated pipelines to seamlessly transition them into production environments. Monitor and optimize performance - the platform provides real-time monitoring dashboards and performance metrics, enabling users to track the accuracy, efficiency and overall effectiveness of deployed models. Collaborate and iterate - features A/B testing and version control to facilitate collaboration and continuous improvement of models, allowing users to refine their models and maximize their impact on business objectives.

What You’ll Do:

Monitor the health, pipelines and alerting of machine learning models deployed on customer’s infrastructure. Respond or alert customers when there has been an outage or issue with one of their models. Incident Management and Priority Classification to make sure the right support team is available to solve the problem, if you can’t solve it yourself. Build quarterly business reviews to provide updates on the health of the ML Models. Evaluate champion/challenger models to see if a new model should be promoted. Ensure there is no bias introduced into the models as new champion models are introduced or additional data is loaded.